research-article

Adapting rule-based model descriptions for simulating in continuous and hybrid space

Authors:

Arne T. Bittig,

Adelinde M. UhrmacherAuthors Info & Claims

CMSB '11: Proceedings of the 9th International Conference on Computational Methods in Systems Biology

Pages 161 - 170

https://doi.org/10.1145/2037509.2037533

Published: 21 September 2011 Publication History

Abstract

Space plays an ever increasing role in cell biological modeling and simulation. This ranges from compartmental dynamics, via mesh-based approaches, to individuals moving in continuous space. An attributed, multi-level, rule-based language, ML-Space, is presented that allows to integrate these different types of spatial dynamics within one model. The associated simulator combines Gillespie's method, the Next Subvolume method, and Brownian dynamics. This allows the simulation of reaction diffusion systems as well as taking excluded volume effects into account. A small example illuminates the potential of the approach in dealing with complex spatial dynamics like those involved in studying the dynamics of lipid rafts and their role in receptor co-localization.

References

[1]

E. Bartocci, F. Corradini, M. R. Di Berardini, E. Merelli, and L. Tesei. Shape calculus. A spatial mobile calculus for 3D shapes. Scientific Annals of Computer Science, 20(20):1--31, 2010.

[2]

D. Bernstein. Simulating mesoscopic reaction-diffusion systems using the gillespie algorithm. Physical Review E, 71(4):041103+, Apr. 2005.

[3]

A. T. Bittig and A. M. Uhrmacher. Spatial modeling in cell biology at multiple levels. In B. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, and E. Yücesan, editors, Proceedings of the 2010 Winter Simulation Conference, pages 608--619. IEEE Computer Science, Dec. 2010.

Digital Library

[4]

L. Cardelli. Brane calculi. In Computational Methods in Systems Biology, pages 257--278, 2005.

Digital Library

[5]

F. Ciocchetta, A. Degasperi, J. Hillston, and M. Calder. Some investigations concerning the ctmc and the ode model derived from bio-pepa. Electr. Notes Theor. Comput. Sci., 229(1):145--163, 2009.

Digital Library

[6]

M. Deaconu and A. Lejay. Simulation of exit times and positions for brownian motions and diffusions. Proc. Appl. Math. Mech., 7(1):1081401--1081402, 2007.

[7]

J. Elf and M. Ehrenberg. Spontaneous separation of bi-stable biochemical systems into spatial domains of opposite phases. Systems biology, 1(2):230--236, Dec. 2004.

[8]

S. Engblom, L. Ferm, A. Hellander, and P. Lötstedt. Simulation of stochastic reaction-diffusion processes on unstructured meshes. ArXiv e-prints, Apr. 2008.

[9]

R. Ewald, J. Himmelspach, M. Jeschke, S. Leye, and A. M. Uhrmacher. Flexible experimentation in the modeling and simulation framework JAMES II--implications for computational systems biology. Brief Bioinform, 11(3):290--300, Jan. 2010.

[10]

D. Fange, O. G. Berg, P. Sjöberg, and J. Elf. Stochastic reaction-diffusion kinetics in the microscopic limit. Proceedings of the National Academy of Sciences, 2010.

[11]

L. Ferm, A. Hellander, and P. Lötstedt. An adaptive algorithm for simulation of stochastic reaction--diffusion processes. Journal of Computational Physics, 229(2):343--360, Jan. 2010.

Digital Library

[12]

G. Gruenert, B. Ibrahim, T. Lenser, M. Lohel, T. Hinze, and P. Dittrich. Rule-based spatial modeling with diffusing, geometrically constrained molecules. BMC Bioinformatics, 11(1):307+, 2010.

[13]

J. F. Hancock. Lipid rafts: contentious only from simplistic standpoints. Nat Rev Mol Cell Biol, 7(6):456--462, June 2006.

[14]

L. A. Harris, J. S. Hogg, and J. R. Faeder. Compartmental rule-based modeling of biochemical systems. In M. D. Rossetti, R. R. Hill, B. Johansson, A. Dunkin, and R. G. Ingalls, editors, Proceedings of the 2009 Winter Simulation Conference, pages 908--919. IEEE Computer Science, Dec. 2009.

Digital Library

[15]

M. Jeschke. Efficient Non-spatial and Spatial Simulation of Biochemical Reaction Networks. PhD thesis, University of Rostock, 18051 Rostock, Oct. 2010.

[16]

M. Jeschke, R. Ewald, and A. M. Uhrmacher. Exploring the performance of spatial stochastic simulation algorithms. J. Comput. Phys., 230:2562--2574, April 2011.

Digital Library

[17]

M. Jeschke and A. M. Uhrmacher. Multi-resolution spatial simulation for molecular crowding. In S. J. Mason, R. R. Hill, L. Moench, and O. Rose, editors, Proceedings of the 2008 Winter Simulation Conference, pages 1384--1392. IEEE, Dec. 2008.

Digital Library

[18]

M. John, R. Ewald, and A. M. Uhrmacher. A spatial extension to the π calculus. Electron. Notes Theor. Comput. Sci., 194(3):133--148, Jan. 2008.

Digital Library

[19]

M. John, C. Lhoussaine, J. Niehren, and A. Uhrmacher. The attributed Pi-Calculus with priorities. Transactions on Computational Systems Biology XII, 5945:13--76, 2010.

Digital Library

[20]

M. John, C. Lhoussaine, J. Niehren, and C. Versari. Biochemical reaction rules with constraints. In G. Barthe, editor, ESOP, volume 6602 of Lecture Notes in Computer Science, pages 338--357. Springer, 2011.

Digital Library

[21]

B. N. Kholodenko. Cell-signalling dynamics in time and space. Nature Reviews Molecular Cell Biology, 7(3):165--176, Feb. 2006.

[22]

D. Lingwood and K. Simons. Lipid rafts as a Membrane-Organizing principle. Science, 327(5961):46--50, Jan. 2010.

[23]

C. Maus, M. John, S. Rybacki, and A. M. Uhrmacher. Towards Rule-Based Multi-Level modeling. Poster, 2010.

[24]

C. Maus, S. Rybacki, and A. M. Uhrmacher. Rule-based multi-level modeling of cell biological systems. submitted, 2011.

[25]

D. V. Nicolau, K. Burrage, R. G. Parton, and J. F. Hancock. Identifying optimal lipid raft characteristics required to promote nanoscale Protein-Protein interactions on the plasma membrane. Mol. Cell. Biol., 26(1):313--323, Jan. 2006.

[26]

C. Priami and P. Quaglia. Beta binders for biological interactions. In Computational Methods in Systems Biology, pages 20--33. Springer Berlin Heidelberg, April 2005.

Digital Library

[27]

K. Radhakrishnan, A. Halász, D. Vlachos, and J. S. Edwards. Quantitative understanding of cell signaling: the importance of membrane organization. Current Opinion in Biotechnology, 21(5):677--682, Oct. 2010.

[28]

A. Regev, E. Panina, W. Silverman, L. Cardelli, and E. Shapiro. Bioambients: an abstraction for biological compartments. Theor. Comput. Sci., 325:141--167, September 2004.

Digital Library

[29]

S. Rybacki, J. Himmelspach, and A. M. Uhrmacher. Experiments with single core, multi-core, and gpu based computation of cellular automata. In Proceedings of the 2009 First International Conference on Advances in System Simulation, pages 62--67, Washington, DC, USA, 2009. IEEE Computer Society.

Digital Library

[30]

K. Takahashi, S. Arjunan, and M. Tomita. Space in systems biology of signaling pathways -- towards intracellular molecular crowding in silico. FEBS Letters, 579(8):1783--1788, Mar. 2005.

[31]

K. Takahashi, S. Tánase-Nicola, and P. R. ten Wolde. Spatio-temporal correlations can drastically change the response of a MAPK pathway. Proceedings of the National Academy of Sciences, 107(6):2473--2478, Feb. 2010.

[32]

D. S. Wishart, R. Yang, D. Arndt, P. Tang, and J. Cruz. Dynamic cellular automata: An alternative approach to cellular simulation. In Silico Biology, 5(2):139--161, Jan. 2005.

Cited By

Andrews S(2019)Rule-Based Modeling Using Wildcards in the Smoldyn SimulatorModeling Biomolecular Site Dynamics10.1007/978-1-4939-9102-0_8(179-202)Online publication date: 4-Apr-2019
https://doi.org/10.1007/978-1-4939-9102-0_8
Helms TWarnke TMaus CUhrmacher A(2017)Semantics and Efficient Simulation Algorithms of an Expressive Multilevel Modeling LanguageACM Transactions on Modeling and Computer Simulation10.1145/299849927:2(1-25)Online publication date: 18-May-2017
https://dl.acm.org/doi/10.1145/2998499
Bittig AUhrmacher A(2017)ML-SpaceIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2016.259816214:6(1339-1349)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1109/TCBB.2016.2598162
Show More Cited By

Index Terms

Adapting rule-based model descriptions for simulating in continuous and hybrid space
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies

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cover image ACM Other conferences

CMSB '11: Proceedings of the 9th International Conference on Computational Methods in Systems Biology

September 2011

224 pages

ISBN:9781450308175

DOI:10.1145/2037509

Conference Chair:
François Fages
INRIA Paris-Rocquencourt, France

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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TCSIM: IEEE Computer Society Technical Committee on Simulation
University Henri-Poincare: University Henri-Poincare - France
NVIDIA
CNRS: Centre National De La Rechercue Scientifique
Microsoft Research: Microsoft Research

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SIGBio: ACM Special Interest Group on Bioinformatics

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 September 2011

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TCSIM
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CNRS
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CMSB'11: 9th International Conference on Computational Methods in Systems Biology

September 21 - 23, 2011

Paris, France

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Cited By

Andrews S(2019)Rule-Based Modeling Using Wildcards in the Smoldyn SimulatorModeling Biomolecular Site Dynamics10.1007/978-1-4939-9102-0_8(179-202)Online publication date: 4-Apr-2019
https://doi.org/10.1007/978-1-4939-9102-0_8
Helms TWarnke TMaus CUhrmacher A(2017)Semantics and Efficient Simulation Algorithms of an Expressive Multilevel Modeling LanguageACM Transactions on Modeling and Computer Simulation10.1145/299849927:2(1-25)Online publication date: 18-May-2017
https://dl.acm.org/doi/10.1145/2998499
Bittig AUhrmacher A(2017)ML-SpaceIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2016.259816214:6(1339-1349)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1109/TCBB.2016.2598162
Wang QClarke E(2016)Formal modeling of biological systems2016 IEEE International High Level Design Validation and Test Workshop (HLDVT)10.1109/HLDVT.2016.7748273(178-184)Online publication date: Oct-2016
https://doi.org/10.1109/HLDVT.2016.7748273
Galpin V(2016)Spatial Representations and Analysis TechniquesAdvanced Lectures of the 16th International School on Formal Methods for the Quantitative Evaluation of Collective Adaptive Systems - Volume 970010.1007/978-3-319-34096-8_5(120-155)Online publication date: 20-Jun-2016
https://dl.acm.org/doi/10.1007/978-3-319-34096-8_5
Mallick P(2016)Complexity and Information: Cancer as a Multi-Scale Complex Adaptive SystemPhysical Sciences and Engineering Advances in Life Sciences and Oncology10.1007/978-3-319-17930-8_2(5-29)Online publication date: 2016
https://doi.org/10.1007/978-3-319-17930-8_2
Warnke THelms TUhrmacher ATaylor SMustafee NSon Y(2015)Syntax and Semantics of a Multi-Level Modeling LanguageProceedings of the 3rd ACM SIGSIM Conference on Principles of Advanced Discrete Simulation10.1145/2769458.2769467(133-144)Online publication date: 10-Jun-2015
https://dl.acm.org/doi/10.1145/2769458.2769467
Luboschik MRöhlig MBittig AAndrienko NSchumann HTominski C(2015)Feature-Driven Visual Analytics of Chaotic Parameter-Dependent MovementComputer Graphics Forum10.1111/cgf.1265434:3(421-430)Online publication date: 1-Jun-2015
https://dl.acm.org/doi/10.1111/cgf.12654
Schützel JPeng DUhrmacher APerrone LBuckley SMiller J(2014)Perspectives on languages for specifying simulation experimentsProceedings of the 2014 Winter Simulation Conference10.5555/2693848.2694208(2836-2847)Online publication date: 7-Dec-2014
https://dl.acm.org/doi/10.5555/2693848.2694208
Bortolussi LNenzi L(2014)Specifying and monitoring properties of stochastic spatio-temporal systems in signal temporal logicProceedings of the 8th International Conference on Performance Evaluation Methodologies and Tools10.4108/icst.Valuetools.2014.258183(66-73)Online publication date: 9-Dec-2014
https://dl.acm.org/doi/10.4108/icst.Valuetools.2014.258183
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